Modern genetic analysis has developed a number of techniques to explore the genetic foundations of traits that go well beyond the traditional approach of finding mutations and seeing what they do. It's now possible to screen an entire genome for sequences associated with a given phenotype, such as heart problems or obesity. Most of these approaches, however, suffer from various issues related to what to do with the information—linking a piece of DNA with a relevant biological function can often be a great challenge. Two papers released yesterday by Nature describe how integrating several of the genome-wide screening techniques can produce powerful information.
One of the papers used mice for the experiments, while the second worked with people; both tackled a major health issue, obesity. The initial technique used was Quantitative Trait Locus analysis, or QTL. This relies on quantitative measurements of specific properties—in this case, measures of obesity such as body-mass index were used. The test subjects were then subjected to a genome-wide analysis of their inheritance, allowing the researchers to identify areas of the genome that are associated with differences in these measures.
These sorts of screens have been performed before, but suffer from a key weakness: knowing an area of a chromosome that may be linked to a trait doesn't necessarily tell you what gene from that region to focus on. To narrow things down, the researchers turned to a different approach that has its own weaknesses: gene expression chips. These chips allow researchers to sample the expression of most of the genes in the genome—the human study, for example, contained sequences matching nearly 24,000 genes. Sample sizes that large, however, suffer from significant numbers of false positives, and it's generally impossible to tell whether any gene that shows differential expression is a cause, effect, or irrelevant to the object of the study.
Combining the two approaches into what the authors termed eQTL (e for expression) got rid of some of the worst problems with each. By mapping the genes on the expression chips back to their locations in the genome, the researchers could associate them with causal genetic factors. And the gene expression data helped the researchers zero in on relevant gene(s) within the genomic regions identified by QTL.
Some of the genes identified by this analysis had functions that weren't clearly linked to obesity, while others had never been examined, and had no known function at all. The researchers got a better picture of their results by performing a network analysis of their data, which revealed sets of genes that were consistently regulated in a coordinated manner. This helped provide a clearer picture of the how many of the genes fit into processes that have previously been implicated in weight regulation.
The mouse study used adipose tissue, with liver results used as a control; the human study used adipose tissue and blood samples. Despite the differences, both studies identified (among other things) a core set of genes that have been implicated in macrophage activity during inflammation. This isn't the first time that inflammation has been associated with obesity, but these results seem to create a more detailed picture of the connection.